<p>This work
demonstrates how we can extract clinically useful patterns</p><p>extracted
from time series data (speech signals) using nonlinear signal<br />
processing and how to exploit those patterns using robust statistical<br />
machine learning tools, in order to estimate remotely and accurately<br />
average Parkinson's disease symptom severity.&nbsp;</p>
<p>&nbsp;</p>

Following work done by the 'Oxford Spies' we uncover more secrets of 'surface-active Agents'. In modern-day applications we refer to these agents as surfactants, which are now extensively used in industrial, chemical, biological and domestic applications. Our work focuses on the dynamic behaviour of surfactant and polymer-surfactant mixtures.

In this talk we propose a mathematical model that incorporates the effects of diffusion, advection and reactions to describe the dynamic behaviour of such systems and apply the model to the over-flowing-cylinder experiment (OFC). We solve the governing equations of the model numerically and, by exploiting large parameters in the model, obtain analytical asymptotic solutions for the concentrations of the bulk species in the system. Thus, these solutions uncover secrets of the 'surface-active Agents' and provide an important insight into the system behaviour, predicting the regimes under which we observe phase transitions of the species in the system. Finally, we suggest how our models can be extended to uncover the secrets of more complex systems in the field.

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide, demanding a response from scientists and clinicians to understand its aetiology and develop effective treatment. CRC is thought to originate via genetic alterations that cause disruption to the cellular dynamics of the crypts of Lieberkűhn, test-tube shaped glands located in both the small and large intestine, which are lined with a monolayer of epithelial cells. It is believed that during colorectal carcinogenesis, dysplastic crypts accumulate mutations that destabilise cell-cell contacts, resulting in crypt buckling and fission. Once weakened, the corrupted structure allows mutated cells to migrate to neighbouring crypts, to break through to the underlying tissue and so aid the growth and malignancy of a tumour. To provide further insight into the tissue-level effects of these genetic mutations, a multi-scale model of the crypt with a realistic, deformable geometry is required. This talk concerns the progress and development of such a model, and its usefulness as a predictive tool to further the understanding of interactions across spatial scales within the context of colorectal cancer.

Graphics processing units (GPU) are well suited to decrease the
computational in-
tensity of stochastic simulation of chemical reaction systems. We
compare Gillespie’s
Direct Method and Gibson-Bruck’s Next Reaction Method on GPUs. The gain
of the
GPU implementation of these algorithms is approximately 120 times faster
than on a
CPU. Furthermore our implementation is integrated into the Systems
Biology Toolbox
for Matlab and acts as a direct replacement of its Matlab based
implementation.

The visceral endoderm (VE) is an epithelium of approximately 200 cells
encompassing the early post-implantation mouse embryo. At embryonic day
5.5, a subset of around 20 cells differentiate into morphologically
distinct tissue, known as the anterior visceral endoderm (AVE), and
migrate away from the distal tip, stopping abruptly at the future
anterior. This process is essential for ensuring the correct orientation
of the anterior-posterior axis, and patterning of the adjacent embryonic
tissue. However, the mechanisms driving this migration are not clearly
understood. Indeed it is unknown whether the position of the future
anterior is pre-determined, or defined by the movement of the migrating
cells. Recent experiments on the mouse embryo, carried out by Dr.
Shankar Srinivas (Department of Physiology, Anatomy and Genetics) have
revealed the presence of multicellular ‘rosettes’ during AVE migration.
We are developing a comprehensive vertex-based model of AVE migration.
In this formulation cells are treated as polygons, with forces applied
to their vertices. Starting with a simple 2D model, we are able to mimic
rosette formation by allowing close vertices to join together. We then
transfer to a more realistic geometry, and incorporate more features,
including cell growth, proliferation, and T1 transitions. The model is
currently being used to test various hypotheses in relation to AVE
migration, such as how the direction of migration is determined, what
causes migration to stop, and what role rosettes play in the process.

Abstract: Nonlinear models have been widely employed to characterize the
underlying structure in a time series. It has been shown that the
in-sample fit of nonlinear models is better than linear models, however,
the superiority of nonlinear models over linear models, from the
perspective of out-of-sample forecasting accuracy remains doubtful. We
compare forecast accuracy of nonlinear regime switching models against
classical linear models using different performance scores, such as root
mean square error (RMSE), mean absolute error (MAE), and the continuous
ranked probability score (CRPS). We propose and investigate the efficacy
of a class of simple nonparametric, nonlinear models that are based on
estimation of a few parameters, and can generate more accurate forecasts
when compared with the classical models. Also, given the importance of
gauging uncertainty in forecasts for proper risk assessment and well
informed decision making, we focus on generating and evaluating both point
and density forecasts.
Keywords: Nonlinear, Forecasting, Performance scores.

<i>The background for the multitarget tracking problem is presented
along with a new framework for solution using the theory of random
finite sets. A range of applications are presented including submarine
tracking with active SONAR, classifying underwater entities from audio
signals and extracting cell trajectories from biological data.</i>

<span style="font-style: normal; font-variant: normal; font-weight: normal; font-size: 8px; line-height: normal; font-size-adjust: none; font-stretch: normal; font-family: Helvetica"><span style="font-size: small" class="Apple-style-span"><span class="Apple-style-span" style="font-size: 12px">Soft
(fuzzy) clustering techniques are often used in the study of
high-dimensional datasets, such as microarray and other high-throughput
bioinformatics data. The most widely used method is Fuzzy C-means
algorithm (FCM), but it can present difficulties when dealing with
nonlinear clusters. In this talk, we will overview and compare
different clustering methods. We will introduce DifFUZZY, a novel
spectral fuzzy clustering algorithm applicable to a larger class of
clustering problems than FCM. This method is better at handling
datasets that are curved, elongated or those which contain clusters of
different dispersion. We will present examples of datasets (synthetic
and real) <span class="Apple-style-span" style="font-size: medium"><span style="font-style: normal; font-variant: normal; font-weight: normal; font-size: 8px; line-height: normal; font-size-adjust: none; font-stretch: normal; font-family: Helvetica"><span style="font-size: small" class="Apple-style-span"><span class="Apple-style-span" style="font-size: 12px">for which this method outperforms other frequently used algorithms</span></span></span></span></span></span></span>

Hairsine-Rose (HR) model is the only multi sediment size soil erosion
model. The HR model is modifed by considering the effects of sediment bedload and
bed elevation. A two step composite Liska-Wendroff scheme (LwLf4) which
designed for solving the Shallow Water Equations is employed for solving the
modifed Hairsine-Rose model. The numerical approximations of LwLf4 are
compared with an independent MOL solution to test its validation. They
are also compared against a steady state analytical solution and experiment
data. Buffer strip is an effective way to reduce sediment transportation for
certain region. Modifed HR model is employed for solving a particular buffer
strip problem. The numerical approximations of buffer strip are compared
with some experiment data which shows good matches.